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Aspects of Online Learning

Date

Authors

Harrington, Edward

Abstract

Online learning algorithms have several key advantages compared to their batch learning algorithm counterparts. This thesis investigates several online learning algorithms and their application. The thesis has an underlying theme of the idea of combining several simple algorithms to give better performance. In this thesis we investigate: combining weights, combining hypothesis, and (sort of) hierarchical combining.¶ ...

Keywords

online learning algorithms, perceptron, large margin classifiers, Bayes point machine, BPM, online Bayes point machine, OBPM, tracking experts, fixed share hierarchy algorithm, FSH, channel equalization, equalizer, line voting, ranking algorithms

URI

http://hdl.handle.net/1885/47147

Collections

Open Access Theses

Type

DOI

10.25911/5d7a29e414896

Downloads

File

Description

01front.pdf (177.92 KB)

02whole.pdf (2.74 MB)